Kyle Rozic
4/21/2021
Data from Kaggle
bostonhousing <- read.csv('./data/housing.csv', header = F, sep = '')
colnames(bostonhousing) <- c('CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV')
head(bostonhousing)## CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX PTRATIO B LSTAT
## 1 0.00632 18 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 4.98
## 2 0.02731 0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 396.90 9.14
## 3 0.02729 0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 4.03
## 4 0.03237 0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 2.94
## 5 0.06905 0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 396.90 5.33
## 6 0.02985 0 2.18 0 0.458 6.430 58.7 6.0622 3 222 18.7 394.12 5.21
## MEDV
## 1 24.0
## 2 21.6
## 3 34.7
## 4 33.4
## 5 36.2
## 6 28.7
plot_ly(bostonhousing,
x = bostonhousing$CRIM,
y = bostonhousing$MEDV,
z = bostonhousing$RM,
type = 'scatter3d',
mode = 'markers',
alpha = 0.5,
color = bostonhousing$RAD,
size = bostonhousing$TAX)## Warning: `line.width` does not currently support multiple values.
This was mainly an experiment to visualize many variables in 1 plot. It starts to get pretty confusing when this many variables are in the plot but it may be useful depending on the data. With this data I can see that a high accessibility to radial highways (RAD, yellow) correlates well with per capita crime rate (CRIM, x).
Have A Great Day Stranger!